view toolboxes/FullBNT-1.0.7/netlab3.3/rbfsetbf.m @ 0:e9a9cd732c1e tip

first hg version after svn

function net = rbfsetbf(net, options, x)
%RBFSETBF Set basis functions of RBF from data.
%
%	Description
%	NET = RBFSETBF(NET, OPTIONS, X) sets the basis functions of the RBF
%	network NET so that they model the unconditional density of the
%	dataset X.  This is done by training a GMM with spherical covariances
%	using GMMEM.  The OPTIONS vector is passed to GMMEM. The widths of
%	the functions are set by a call to RBFSETFW.
%
%	See also
%	RBFTRAIN, RBFSETFW, GMMEM
%

%	Copyright (c) Ian T Nabney (1996-2001)

errstring = consist(net, 'rbf', x);
if ~isempty(errstring)
  error(errstring);
end

% Create a spherical Gaussian mixture model
mix = gmm(net.nin, net.nhidden, 'spherical');

% Initialise the parameters from the input data
% Just use a small number of k means iterations
kmoptions = zeros(1, 18);
kmoptions(1) = -1;	% Turn off warnings
kmoptions(14) = 5;  % Just 5 iterations to get centres roughly right
mix = gmminit(mix, x, kmoptions);

% Train mixture model using EM algorithm
[mix, options] = gmmem(mix, x, options);

% Now set the centres of the RBF from the centres of the mixture model
net.c = mix.centres;

% options(7) gives scale of function widths
net = rbfsetfw(net, options(7));